Improving Geostationary Satellite-Based Prediction of Wildfire Spread by Directional Rate of Spread Adjustment Factor

This study proposes a “Directional” Rate of Spread (ROS) adjustment factor approach, which applies different ROS adjustment factors depending on the location of the wildfire spread region, even for the same fuel model. Using the observation data gathered by geostationary satellites, the directional ROS adjustment factor is estimated based on the least-squares method. In addition, a new methodology is proposed to indirectly improve the prediction accuracy of regional-scale wildfire spread simulators by using a directional ROS adjustment factor. Since geostationary satellites can obtain observation data at short time intervals, we identify the relationship between the directional ROS adjustment factor and correct the adjustment factors based on the locations and times of the newly detected pixels in the geostationary satellite data and the fuel model distribution of the surrounding pixels. The proposed methodology is demonstrated through the 2020 Creek Fire in California, United States. The results confirm that the proposed methodology and the directional ROS adjustment factor can significantly increase the wildfire spread prediction accuracy.